Browse > Article
http://dx.doi.org/10.12989/cac.2021.27.1.063

The effect of the new stopping criterion on the genetic algorithm performance  

Kaya, Mustafa (Faculty of Engineering, Aksaray University)
Genc, Asim (TUSAS-Kazan Vocational School, Gazi University)
Publication Information
Computers and Concrete / v.27, no.1, 2021 , pp. 63-71 More about this Journal
Abstract
In this study, a new stopping criterion, called "backward controlled stopping criterion" (BCSC), was proposed to be used in Genetic Algorithms. In the study, the available stopping citeria; adaptive stopping citerion, evolution time, fitness threshold, fitness convergence, population convergence, gene convergence, and developed stopping criterion were applied to the following four comparison problems; high strength concrete mix design, pre-stressed precast concrete beam, travelling salesman and reinforced concrete deep beam problems. When completed the analysis, the developed stopping criterion was found to be more accomplished than available criteria, and was able to research a much larger area in the space design supplying higher fitness values.
Keywords
genetic algorithm; genetic algorithm operators; stopping criteria;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Kaya, M. (2001), "Design of reinforced concrete deep beams using genetic algorithms", Gazi Universty Institute of Science and Technology, Ankara.
2 Kaya, M. (2011), "The effects of a new selection operator on the performance of genetic algorithm", Appl. Math. Comput., 217, 7669-7678. https://doi.org/10.1016/j.amc.2011.02.070.   DOI
3 Kaya, M. (2011), "The effects of two new crossover operators on genetic algorithm performance", Appl. Soft Comput., 11, 881-890. https://doi.org/10.1016/j.asoc.2010.01.008.   DOI
4 Kaya, M. (2018), "Developing a new mutation operator to solve the RC deep beam problems by aid of genetic algorithm", Comput. Concrete, 22(5), 493-500. https://doi.org/10.12989/cac.2018.22.5.493.   DOI
5 Kukkonen, S. and Lampinen, J. (2004), "An extension of generalized differential evolution for multi-objective optimization with constraints", International Conference on Parallel Problem Solving from Nature, Berlin, Heidelberg, 752-761.
6 Li, H., Jiao, Y.C. and Zhang, L. (2010), "Hybrid differential evolution with a simplified quadratic approximation for constrained optimization problems", Eng. Optim., 43(2), 115-134. https://doi.org/10.1080/0305215X.2010.481021.   DOI
7 Marti, L., Garci, J., Berlanga, A. and Molina, J.M. (2016), "A stopping criterion for multi-objective optimization evolutionary algorithms", Inform. Sci., 367-368(1), 700-718. https://doi.org/10.1016/j.ins.2016.07.025.   DOI
8 Marti, L., Garcia, J., Berlanga, A. and Molina, J.M. (2009), "An approach to stopping criteria for multi-objective optimization, evolutionary algorithms: The MGBMcriterion", IEEE Congress on Evolutionary Computation, 1263-1270.
9 Neuro Dimension (2014), http:/www.google.com.tr/?gws_rd=ssl#q=genetic+algorithm+cutting+criterias.
10 ACI 318-99 (1999), American Concrete Institute.
11 Parichatprecha, R. and Nimityongskul, P. (2009), "An integrated approach for optimum design of HPC mix proportion using genetic algorithm and artificial neural networks", Comput. Concrete, 6(3), 253-268. https://doi.org/10.12989/cac.2009.6.3.253.   DOI
12 Park, W.J., Noguchi, T. and Lee, H.S. (2013), "Genetic algorithm in mix proportion design of recycled aggregate concrete. Comput. Concrete, 11(3), 183-199. https://doi.org/10.12989/cac.2013.11.3.183.   DOI
13 Rangaiah, G.P., Sharma, S. and Lin, H.W. (2017), "Evaluation of two termination criteria in evolutionary algorithms for multiobjective optimization of complex chemical processes", Chem. Eng. Res. Des., 124, 58-65. https://doi.org/10.1016/j.cherd.2017.05.030.   DOI
14 Rudenko, O. and Schoenauer, M. (2004), "A steady performance stopping criterion for Pareto-based evolutionary algorithm", Proceedings of the 6th Int. Multi-objective Programming and Goal Programming.
15 Sgambi, L., Gkoumas, K. and Bontempi, F. (2014), "Genetic algorithm optimization of precast hollow core slabs", Comput. Concrete, 13(3), 389-409. https://doi.org/10.12989/cac.2014.13.3.389.   DOI
16 Sharma, S. and Rangaiah, G.P. (2013), "An improved multiobjective differential evolution with a cutting criterion for optimizing chemical processes", Comput. Chem. Eng., 56, 155-173. https://doi.org/10.1016/j.compchemeng.2013.05.004.   DOI
17 Sindhya, K., Deb, K. and Miettinen, K. (2008), "A local search based evolutionary multi-objective optimization approach for fast and accurate convergence", Lecture Notes in Computer Science, 815-824.
18 Van Veldhuizen, D.A. and Lamont, G.B. (1998), "Evolutionary computation and conver-gence to a Pareto front",
19 Sugunthan, P.N. (2007), "Report on performance assessment of multi objective optimization algorityhms", CEC Special Session on the Performance Assessment of Real Paremeter MOEAs.
20 Trautmann, H., Ligges, U., Mehnen, J. and Preuss, M. (2008), "A convergence criterion for multi-objective evolutionary algorithms based on systematic statistical testing", Lecture Notes in Computer Science, 825-836.
21 Wagner, T. and Trautmann, H. (2009), "Online convergence detection for evolutionary multi-objective algorithms revisited", IEEE Congress on Evolutionary Computation, 1-8.
22 Wagner, T., Trautmann, H. and Naujoks, B. (2009), "OCD: Online convergence detection for evolutionary multi-objective algorithms based on statistical testing", Lecture Notes in Computer Science, 198-215.
23 Wang, Y.N., Wu, L.H. and Yuan, X.F. (2010), "Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy based diversity measure", Soft Comput., 14, 193-209. https://doi.org/10.1007/s00500-008-0394-9.   DOI
24 Webb-Robertson, B.J.M., Jarman, K.H., Harvey, S.D., Posse, C. and Wright, B.W. (2005), "An improved optimization algorithm and a Bayes factor termination criterion for sequential projection pursuit", Chemom. Intel. Lab. Syst., 77(1-2), 149-160.   DOI
25 Zitzler, E. and Thiele, L. (1998), "Multi-objective optimization using evolutionary algorithms: A comparative case study", International Conference on Parallel Problem Solving from Nature, Springer, Berlin, Heidelberg, 292-301.
26 Wong, J.Y., Sharma, S. and Rangaiah, G.P. (2016), "Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with termination criteria", Appl. Therm. Eng., 93, 888-899. https://doi.org/10.1016/j.applthermaleng.2015.10.055.   DOI
27 Zhang, J. and Sanderson, A.C. (2008), "Self-adaptive multiobjective differential evolution with the directional information provided by archived inferior solutions", IEEE Congress on Evolutionary Computation, 2801-2810.
28 Zhang, Q., Zhou, A., Zhano, S., Suganthan, P.N., Liu, W. and Tiwari, S. (2009), "Multi-objective optimization test instances for the CEC 2009 special session and competition", CEC Special Session on the Performance Assessment of MultiObjective Optimization Algorithms.
29 Zhou, G., Zhang, C., Lu, F. and Zhang, J. (2020), "Integrated optimization of cutting parameters and tool path for cavity milling considering carbon emissions", J. Clean. Prod., 250, 119454. https://doi.org/10.1016/j.jclepro.2019.119454.   DOI
30 Zitzler, E. and Thiele, L. (1998), "Multi-objective optimization using evolutionary algorithms: A comparative case study", International Conference on Parallel Problem Solving from Nature, Berlin, Heidelberg, 292-301.
31 Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M. and Fonseca, V.G. (2003), "Perfor-mance assessment of multi-objective optimizers: An analysis and review", IEEE Tran. Evol. Comput., 7(2), 117-132. https://doi.org/10.1109/TEVC.2003.810758.   DOI
32 Erdogan, Y.S. and Bakir, P.G. (2013), "Evaluation of the different genetic algorithm parameters and operators for the finite element model updating problem", Comput. Concrete, 11(6), 541-569. https://doi.org/10.12989/cac.2013.11.6.541.   DOI
33 Ali, M., Siarry, P. and Pant, M. (2012), "An efficient differential evaluation based algorithm for solving multi-objective optimization problems", Eur. J. Operat. Res., 217, 404-416. https://doi.org/10.1016/j.ejor.2011.09.025.   DOI
34 Chen, L.C., Luh, C.J. and Jou, C. (2005), "Generating page clippings from web search results using a dynamically terminated genetic algorithm", Inform. Syst., 30(4), 299-316. https://doi.org/10.1016/j.is.2004.04.002.   DOI
35 da Silva, W.R.L. and Stemberk, P. (2013), "Genetic-fuzzy approach to model concrete shrinkage", Comput. Concrete, 12(2), 109-129. https://doi.org/10.12989/cac.2013.12.2.109.   DOI
36 Deb, K., Agarwal, S., Pratap, A. and Meyarivan, T. (2000), "A fast and elitist multi-objective genetic algorithm: NSGA-II", Technical Report 200001, IIT Kanpur, KanGAL.
37 Deb, K., Agarwal, S., Pratap, A. and Meyarivan, T. (2000), "A fast and elitist multi-objectivegenetic algorithm: NSGA-II", Technical Report 200001, IIT Kanpur, KanGAL.
38 Haeser, G. and Melo, V. (2015), "Convergence detection for optimization algorithms: Approximate-KKT stopping criterion when Lagrange multipliers are not available", Operat. Res. Lett., 43(5), 484-488. https://doi.org/10.1016/j.orl.2015.06.009.   DOI
39 Justin, Y.Q.W., Shivom, S. and Rangaiah. G.H. (2016), "Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with cutting criteria", Appl. Therm. Eng., 93, 888-899. https://doi.org/10.1016/j.applthermaleng.2015.10.055.   DOI